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Record W4226119767 · doi:10.1038/s41537-022-00246-8

Progressive changes in descriptive discourse in First Episode Schizophrenia: a longitudinal computational semantics study

2022· article· en· W4226119767 on OpenAlexafffund
María Francisca Alonso-Sánchez, Sabrina D. Ford, Michael Mackinley, Angélica Silva, Roberto Limongi, Lena Palaniyappan

Bibliographic record

VenueSchizophrenia · 2022
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsMcGill UniversityDouglas Mental Health University InstituteLawson Health Research InstituteRobarts Clinical TrialsWestern University
FundersCHIST-ERASchulich School of Medicine and DentistryRobarts Research InstituteCanadian Institutes of Health ResearchDalhousie UniversityAgencia Nacional de Investigación y DesarrolloChrysalisAgenția Națională pentru Cercetare și DezvoltareMcGill University
KeywordsSchizophrenia (object-oriented programming)PsychologyCognitionSemantics (computer science)Semantic memorySimilarity (geometry)Thought disorderStroop effectConversationCognitive psychologyPsychiatryArtificial intelligenceComputer scienceCommunication

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.035
GPT teacher head0.303
Teacher spread0.267 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2022
Admission routes2
Has abstractyes

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